***************************************************************************************************************************************
*                         Supermarkets benefit women by reducing time spent on unpaid housework                                       *
***************************************************************************************************************************************


log using Regression_Analysis.smcl,replace

cd C:\Users\Jayson\Desktop

use Data2025.dta, clear

xtset idind time

/*
Treatment variable:
SM : Have access to a supermarkets within 5 km (Dummy variable)


Control Variables:
age :            Years of age
age2 :           Squared years of age
lnincome :       ln(Per capita household income (CNY) inflated to 2011 prices)
v29 :            Intensity of activity at work, on a scale of 1 to 5 from very slight activity to very heavy activity
dk :             Score for individual dietary knowledge
hhsize :         Number of family members
agridiversity :  Families' number of farming activities (crop farming, fishing, gardening, and livestock), range (0-4)
car :            Does household own a car? 1 if yes and 0 if no
motorcycle :     Does household own a motorcycle? 1 if yes and 0 if no
market :         Aggregated index indicting the access (distance) to traditional food markets and number of days of operation
restaurant :     Are there any restaurants in or near this community? 1 if yes and 0 if no
econ :           Aggregated index indicating typical daily wage for ordinary male worker and percent of the populations engaged in non-agricultural work
fridge :         Does household own a refrigerator? 1 if yes and 0 if no
microwave :      Does household own a microwave? 1 if yes and 0 if no
pressure_cooker: Does household own a pressure cooker? 1 if yes and 0 if no


Outcomevariables:
shoppingtime: Women's food shopping time (in minutes)
lnshoppingtime: ln(Women's food shopping time)
cookingtime: Women's food cooking time (in minutes)
lncookingtime: ln(Women's food cooking time)

Energy : Individual's calorie intake (Kcal)
Protein : Individual's protein intake (Grams)
Fat : Individual's fat intake (Grams)
Carbohydrate : Individual's carbohydrate intake (Grams)


Energy_unprocessed : Individual's calorie intake from unprocessed foods
Energy_moderate : Individual's calorie intake from moderately processed foods
Energy_highly : Individual's calorie intake from highly processed foods

Protein_unprocessed : Individual's protein intake from unprocessed foods
Protein_moderate : Individual's protein intake from moderately processed foods
Protein_highly : Individual's protein intake from highly processed foods

Fat_unprocessed : Individual's fat intake from unprocessed foods
Fat_moderate : Individual's fat intake from moderately processed foods
Fat_highly : Individual's fat intake from highly processed foods

Carbohydrate_unprocessed : Individual's carbohydrate intake from unprocessed foods
Carbohydrate_moderate : Individual's carbohydrate intake from moderately processed foods
Carbohydrate_highly : Individual's carbohydrate intake from highly processed foods

DietaryDiversity : DDS
DietaryBalance : CFPS
DietaryNutrientDensity : DNDS

EnergyFromFat : Share of energy intake from fat (range, 0-1)
EnergyFromFat30 : Dummy variable: share of energy intake from fat exceeds 30%


CFPS_salt CFPS_oil CFPS_staples CFPS_vegetables CFPS_fruits CFPS_meat CFPS_legumes CFPS_milk : Sub-scores of CFPS

BMI : Body mass index
Overweight : Dummy
Obesity : Dummy

PaidWork :         Participation in paid work (dummy)
lnWorkTime :       ln(Hours spent on paid work per week)
lnOtherHousework : ln(Time spent on other housework)
lnLeisure :        ln(Time for leisure)
lnSleeping :       ln(Time for sleeping)

HHhighincome : High-income households (Dummy)
HighDDS :      High dietary diversity (Dummy)
HighCFPS :     High dietary balance (Dummy)
HighDNDS :     High dietary nutrient density (Dummy)

adequateEnergy : Adequate energy intake (Dummy)
adequateProtein : Adequate protein intake (Dummy)


Fixed effects:
idind :       Individual fixed effects
wave :        Year fixed effects
i.wave#i.t1 : Province-by-year fixed effects


Sample1 = 1 : Sample used for analysis (excluding outliers) (Time for shopping and cooking != 0)
Sample2 = 1 : Sample used for analysis (excluding outliers)
main_shopping : Only keep women who take the main responsibility for shopping in households
main_cooking : Only keep women who take the main responsibility for cooking in households
Anthropometric = 1 : Anthropometric variables are collected.
alwaystreated : Have access to supermarkets in all survey waves (Dummy)


GENDER = 1 if Male, = 2 if Female

*/




global controls1 "age age2 lnincome v29 dk hhsize agridiversity car motorcycle market restaurant"
global controls2 "age age2 lnincome v29 dk hhsize agridiversity car motorcycle market restaurant econ"
global controls3 "age age2 lnincome v29 dk hhsize agridiversity car motorcycle market restaurant econ fridge microwave pressure_cooker"


********************************************************************************
*                                  Figure 2                                    *
********************************************************************************

**Fig. 2a(1)
xthdidregress ra (lnshoppingtime            i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Fig. 2a(2)
xthdidregress ra (lnshoppingtime $controls1 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Fig. 2a(3)
xthdidregress ra (lnshoppingtime $controls2 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Fig. 2a(4)
xthdidregress ra (lnshoppingtime $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 2a(5)
xthdidregress ra (lncookingtime             i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 2a(6)
xthdidregress ra (lncookingtime  $controls1 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 2a(7)
xthdidregress ra (lncookingtime  $controls2 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 2a(8)
xthdidregress ra (lncookingtime  $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 2b
xthdidregress ra (lnshoppingtime $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph


**Fig. 2c
xthdidregress ra (lncookingtime  $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph




********************************************************************************
*                                  Figure 3                                    *
********************************************************************************

**Fig. 3a
xthdidregress ra (Energy             $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig. 3b
xthdidregress ra (Energy_unprocessed $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig. 3c
xthdidregress ra (Energy_moderate    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig. 3d
xthdidregress ra (Energy_highly      $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3e
xthdidregress ra (Protein             $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3f
xthdidregress ra (Protein_unprocessed $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3g
xthdidregress ra (Protein_moderate    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3h
xthdidregress ra (Protein_highly      $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3i
xthdidregress ra (Fat                 $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3j
xthdidregress ra (Fat_unprocessed     $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3k
xthdidregress ra (Fat_moderate        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3l
xthdidregress ra (Fat_highly          $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3m
xthdidregress ra (Carbohydrate        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3n
xthdidregress ra (Carbohydrate_unprocessed $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3o
xthdidregress ra (Carbohydrate_moderate    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation


**Fig.3p
xthdidregress ra (Carbohydrate_highly      $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation, dynamic graph
estat aggregation





********************************************************************************
*                                  Figure 4                                    *
********************************************************************************

**Fig. 4a
*Full sample
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Women
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Men
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low income
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High income
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low DDS
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighDDS==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High DDS
xthdidregress ra (DietaryDiversity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighDDS==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Fig. 4b
*Full sample
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Women
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Men
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low income
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High income
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low CFPS
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighCFPS==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High CFPS
xthdidregress ra (DietaryBalance $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighCFPS==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation



**Fig. 4c
*Full sample
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Women
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Men
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low income
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High income
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HHhighincome==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*Low DNDS
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighDNDS==0 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


*High DNDS
xthdidregress ra (DietaryNutrientDensity $controls3 i.wave#i.t1) (SM) if Sample2==1 & HighDNDS==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


clear



********************************************************************************
*                     Supplementary Figures and Tables                         *
********************************************************************************

use Women2025.dta,clear
**Table S5(1)
xthdidregress ra (lnshoppingtime $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1 & main_shopping==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Table S5(2)
xthdidregress ra (lncookingtime  $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1 & main_cooking==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


clear




use Data2025.dta, clear
**Table S5(3)
xthdidregress ra (shoppingtime $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & GENDER==1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S5(4)
xthdidregress ra (cookingtime  $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & GENDER==1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S7(1)
xthdidregress ra (Energy $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==0, group(idind) vce(cluster Commid_GENDER) //低摄入
estat aggregation


**Table S7(2)
xthdidregress ra (Energy $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==1, group(idind) vce(cluster Commid_GENDER) //高摄入
estat aggregation


**Table S7(3)
xthdidregress ra (Protein $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateProtein==0, group(idind) vce(cluster Commid_GENDER)  //蛋白质不够
estat aggregation


**Table S7(4)
xthdidregress ra (Protein $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateProtein==1, group(idind) vce(cluster Commid_GENDER)  //蛋白质够
estat aggregation


**Table S8(1)
xthdidregress ra (EnergyFromFat   $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)  //全部
estat aggregation

**Table S8(2)
xthdidregress ra (EnergyFromFat30 $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)  //全部
estat aggregation





**Figure S4-Salt
xthdidregress ra (CFPS_salt       $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Edible oil
xthdidregress ra (CFPS_oil        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Staples
xthdidregress ra (CFPS_staples    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Vegetables
xthdidregress ra (CFPS_vegetables $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Fruits
xthdidregress ra (CFPS_fruits     $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Meat, poultry, and egg
xthdidregress ra (CFPS_meat       $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Legumes and nuts
xthdidregress ra (CFPS_legumes    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation

**Figure S4-Milk
xthdidregress ra (CFPS_milk       $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation






**Table S9(1) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)  
estat aggregation

**Table S9(1) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)  
estat aggregation

**Table S9(1) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)  
estat aggregation


**Table S9(2) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(2) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(2) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==2 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S9(3) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(3) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(3) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & GENDER==1 & alwaystreated!=1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S9(4) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(4) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(4) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(5) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(5) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(5) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & HHhighincome==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(6) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(6) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(6) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==0 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(7) BMI
xthdidregress ra (BMI        $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(7) Overweight
xthdidregress ra (Overweight $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S9(7) Obesity
xthdidregress ra (Obesity    $controls3 i.wave#i.t1) (SM) if Sample2==1 & alwaystreated!=1 & adequateEnergy==1 & Anthropometric==1, group(idind) vce(cluster Commid_GENDER)
estat aggregation






**Table S10(1)
xthdidregress ra (PaidWork         $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1 , group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S10(2)
xthdidregress ra (PaidWork         $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1 & age<=55, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S10(3)
xthdidregress ra (lnWorkTime       $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S10(4)
xthdidregress ra (lnWorkTime       $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1 & age<=55, group(idind) vce(cluster Commid_GENDER) 
estat aggregation


**Table S10(5)
xthdidregress ra (lnOtherHousework $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation


**Table S10(6)
xthdidregress ra (lnLeisure        $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER) 
estat aggregation

**Table S10(7)
xthdidregress ra (lnSleeping       $controls3 i.wave#i.t1) (SM) if Sample1==1 & GENDER==2 & alwaystreated!=1, group(idind) vce(cluster Commid_GENDER)
estat aggregation



clear

log close





